Product manufacture-oriented combined optimization method

Abstract

The present invention discloses a product manufacture-oriented combined optimization method. The combined optimization method enables the quality-cost control function in a product manufacturing environment. On the premise that the manufacturing cost is controlled not to exceed a target cost, the product quality is ensured to be optimal. The product diversity is further improved. According to the technical scheme of the invention, the chaotic initialization and chaotic upset strategy is adopted, and the advantages and disadvantages of a product combination scheme are evaluated based on a personalized fitness function. Combined items are classified, so that the number of combination is reduced. In this way, the complete enumeration of all product combination schemes is enabled, so that the search efficiency is improved. Meanwhile, a personalized interface is provided, and a personalized combination scheme of optimal cost performance is generated according to the personalized requirements of users. During the searching process of the optimal product combination scheme, the chaotic search is conducted and all possible optimum product combination schemes are fully traversed. Therefore, the diversity of product combination schemes is ensured.

Description

technical field [0001] The invention relates to a combined optimization method oriented to product manufacturing, and belongs to the technical field of computer and manufacturing cross-application. Background technique [0002] When enterprises are engaged in fierce market competition, the cost of products is closely related to the survival and development of enterprises. Cost reduction has always been the goal that enterprises strive to pursue. The key to effectively reducing the product cost of our country's enterprises is to control manufacturing costs. As new technologies develop faster than imagined, and people's needs become more personalized, the challenges facing enterprises are becoming more and more complex. In recent years, the manufacturing industry is faced with the situation that the product price is continuously reduced, the quality requirements are continuously improved, the delivery time is continuously shortened, and the requirements for variety individual...

AI Technical Summary

Problems solved by technology

[0005] 1) The random search strategy is used in the solution process, which does not have a good global search ability, resulting in premature combination schemes, which cannot effectively cope with the variety requirements faced by product manufacturing;

[0006] 2) It does not provide a personalized interface or is not based on multi-objective constraints, so it cannot effectively solve discrete, multi-objective, and personalized combinatorial optimization problems, and it cannot effectively solve the difficulties faced by the manufacturing industry

The existing combinatorial optimization methods use random search strategies in the solution process, which do not have good global search capabilities, resulting in premature combinatorial schemes and cannot effectively cope with the diverse requirements of product manufacturing. Combinatorial optimization problems are typical NP-hard problems. The time complexity of the problem is polynomial time, as the scale of the problem expands, it will lead to combinatorial explosion, so the efficiency of combinatorial optimization methods is a key factor to be considered; there is still a common problem in existing combinatorial optimization methods, that is, they do not provide individual Interface or not based on multi-objective constraints, all cannot effectively solve discrete, multi-objective, personalized combinatorial optimization problems, and cannot effectively solve the difficulties facing the manufacturing industry

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